Mass spectrometry (MS) has been one of the most widely used tools for bioanalytical analysis due to its high sensitivity, capability of quantitative analysis, and compatibility with biomolecules. Among various MS techniques, single cell mass spectrometry (SCMS) is an advanced approach to molecular analysis of cellular contents in individual cells. In tandem with the creation of novel experimental techniques, the development of new SCMS data analysis tools is equally important. As most published software packages are not specifically designed for pretreatment of SCMS data, including peak alignment and background removal, their applicability on processing SCMS data is generally limited. Hereby we introduce a Python platform, MassLite, specifically designed for rapid SCMS metabolomics data pretreatment. This platform is made user-friendly with graphical user interface (GUI) and exports data in the forms of each individual cell for further analysis. A core function of this tool is to use a novel peak alignment method that avoids the intrinsic drawbacks of traditional binning method, allowing for more effective handling of MS data obtained from high resolution mass spectrometers. Other functions, such as void scan filtering, dynamic grouping, and advanced background removal, are also implemented in this tool to improve pretreatment efficiency.

中文翻译：

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MassLite：用于单细胞质谱代谢组学数据预处理的集成 Python 平台，具有图形用户界面和先进的峰对齐方法


质谱 （MS） 因其高灵敏度、定量分析能力以及与生物分子的兼容性而成为生物分析中使用最广泛的工具之一。在各种 MS 技术中，单细胞质谱 （SCMS） 是一种对单个细胞中细胞内容物进行分子分析的先进方法。在创建新颖实验技术的同时，开发新的 SCMS 数据分析工具同样重要。由于大多数已发布的软件包并非专门为 SCMS 数据的预处理而设计，包括峰对齐和背景去除，因此它们在处理 SCMS 数据方面的适用性通常受到限制。在此，我们介绍一个 Python 平台 MassLite，它专为快速 SCMS 代谢组学数据预处理而设计。该平台通过图形用户界面 （GUI） 实现用户友好，并以每个单元的形式导出数据以供进一步分析。该工具的核心功能是使用一种新颖的峰对齐方法，该方法避免了传统分档方法的固有缺点，从而可以更有效地处理从高分辨率质谱仪获得的 MS 数据。该工具还实现了其他功能，例如空隙扫描过滤、动态分组和高级背景去除，以提高预处理效率。